منابع مشابه
Choosing a Coverage Probability for Prediction Intervals
Coverage probabilities for prediction intervals are germane to filtering, forecasting, previsions, regression, and time series analysis. It is a common practice to choose the coverage probabilities for such intervals by convention or by astute judgment. We argue here that coverage probabilities can be chosen by decision theoretic considerations. But to do so, we need to specify meaningful utili...
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A Poisson distribution is well used as a standard model for analyzing count data. So the Poisson distribution parameter estimation is widely applied in practice. Providing accurate confidence intervals for the discrete distribution parameters is very difficult. So far, many asymptotic confidence intervals for the mean of Poisson distribution is provided. It is known that the coverag...
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Prediction interval is awidely used tool in industrial applications to predict the distribution of future observations. The exact minimum coverage probability and the average coverage probability of the conventional prediction interval for a discrete random variable have not been accurately derived in the literature. In this paper, procedures to compute the exact minimumconfidence levels and th...
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A new method is proposed for the correction of confidence intervals when the original interval does not have the correct nominal coverage probabilities in the frequentist sense. The proposed method is general and does not require any distributional assumptions. It can be applied to both frequentist and Bayesian inference where interval estimates are desired. We provide theoretical results for t...
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ژورنال
عنوان ژورنال: Journal of Research of the National Institute of Standards and Technology
سال: 2021
ISSN: 2165-7254
DOI: 10.6028/jres.126.004